2020/10/13 by Aymen Al Saadi, Dario Alfè, Saadi, Aymen Al +68 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Drug Discovery Methods #Computational Engineering #Distributed #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Gene Regulatory Network Analysis #Genetics, Bioinformatics, and Biomedical Research #Parallel #Quantitative Methods (q-bio.QM) #and Cluster Computing (cs.DC) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2010.06574
openalex publication_date 2020/10/13 · openalex created_date 2022/08/28 · openalex updated_date 2026/07/28
The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and 2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine.